Liver Disease Prediction using Machine Learning

The objective of this project to create a ML Model to predict the liver disease from the huge liver disease datasets using the algorithms. Time complexity and accuracy can measured by various machine learning models ,so that we can measures different. Risky factors can be predicted early by machine learning models.

Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation


Liver Disease Prediction using Machine Learning

Liver diseases are becoming one of the most fatal diseases in several countries. Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. liver patient datasets are investigate for building classification models in order to predict liver disease. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. In that paper, we proposed as checking the whole patient Liver Disease using Machine learning algorithms. Chronic liver disease refers to disease of the liver which lasts over a period of six months.? So in that, we will take results of how much percentage patients get disease as a positive information and negative information. Using classifiers, we are processing Liver Disease percentage and values are showing as a confusion matrix.?We proposed a various classification scheme which can effectively improve the classification performance in?the situation that training dataset is available. In that dataset, we have nearly 500 patient details. We will get all that details from there. Then we will good and bad values are using machine learning? classifier. Thus outputs shows from proposed classification model indicate that Accuracy in predicting the result. Liver Disease Prediction using Machine Learning


Typically, the existing mechanisms assumed that the accuracy of prediction was achieved. But this wasn?t the case then, hence, it must be improved further to increase the classification accuracy. Also, other research works addressed these issues by introducing efficient combination. Existing Models based on feature selection and classification raised some issues regarding with training dataset and Test dataset.


  • Certain approaches being applicable only for small data.
  • Certain combination of classifier over fit with data set while others are under fit.
  • Some approaches are not adoptable for real time collection of database implementation.


Machine learning has attracted a huge amount of researches and has been applied in various fields in the world. In medicine, machine learning has proved its power in which it has been employed to solve many emergency problems such as cancer treatment, heart disease, dengue fever diagnosis and so on. In proposed system , we have to import the liver patient dataset (.csv). Then the dataset should be pre-processed and remove the anomalies and full up empty cells in the dataset , so the we can further improve the effective Liver diseases prediction. Then we are Confusion matrix – For getting a better clarity of the no of correct/incorrect predictions by the model ROC-AUC – It considers the rank of the output probabilities and intuitively measures the likelihood that model can distinguish between a positive point and a negative point. (Note: ROC-AUC is typically used for binary classification only). We will use AUC to select the best model among the various machine learning models.


  • the performance classification of liver based diseases is further improved.
  • Time complexity and accuracy can measured by various machine learning models ,so that we can measures different .
  • Different machine learning having high accuracy of result.
  • Risky factors can be predicted early by machine learning models.




  1. Windows 7,8,10 64 bit
  2. RAM 4GB


  1. Data Set
  2. Python 2.7
  3. Anaconda Navigator

Python’s standard library

  • Pandas
  • Numpy
  • Sklearn
  • seaborn
  • matplotlib

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